State of the Art in Mobile Robot Localization

Dear Colleagues,

Accurate and robust localization is a fundamental prerequisite for autonomous mobile robots, directly influencing navigation, decision-making, and task execution. Over the past two decades, research has advanced from classical probabilistic frameworks toward sophisticated approaches that integrate sensor fusion, optimization, and machine learning. Nevertheless, persistent challenges remain, including operation under uncertainty, adaptability to unstructured and dynamic environments, scalability in large-scale deployments, and computational efficiency for real-time applications.

Recent developments in multi-modal sensing, graph-based optimization, deep learning, and cooperative multi-robot systems have reinvigorated localization research, providing novel methodologies that push the boundaries of robustness and generalization. This Special Issue aims to consolidate state-of-the-art progress, highlight emerging paradigms, and identify open problems that will shape the next generation of autonomous systems. We invite original research articles, surveys, and comparative studies that advance the theoretical foundations, algorithmic innovations, and experimental validations of mobile robot localization.

We look forward to receiving your contributions.

Prof. Dr. José Miguel Guerrero Hernández
Dr. Francisco Martin-Rico
Dr. Rodrigo Pérez-Rodríguez
Guest Editors

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